Panel: PAN-3: Signal and Information Processing Advances for Federated Learning
Moderators: Mingyi Hong, University of Minnesota, USA, Anthony Kuh, University of Hawaii, USA Panelists: Soummya Kar, Carnegie Mellon University, USA H. Vincent Poor, Princeton University, USA Michael Rabbat, Meta Platforms Inc, USA Anna Scaglione, Cornell University, USA Alex Sprintson, Texas A&,M University, USA
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26 May 2022
Advances in hardware and a proliferation of applications for edge devices and systems (mobile phones, sensor networks, IoT devices) have led to more processing and learning at the edge. In particular, Federated Learning (FL) where data gathering and learning takes place at the edge devices and systems have become key research areas in both academia and industry. Additional concerns that Federated Learning addresses include privacy, communications, heterogeneous systems, and heterogeneous data. This panel addresses signal and information processing advances for Federated Learning. Some of the issues that will be addressed include; key technical innovations that drive FL research, FL use cases in industry, emerging applications in FL, and future directions of FL research.
